A device for bi-directional audio-based communication typically includes both a loudspeaker and a microphone. The loudspeaker is used to play back audio signals received from a remote (“far-end”) source, while the microphone is used to capture audio signals from a local (“near-end”) source. In the case of a telephone call, for example, the near- and far-end sources may be people engaged in a conversation, and the audio signals may contain speech. An acoustic echo occurs when the far-end signal emitted by the loudspeaker is captured by the microphone, after undergoing reflections in the local environment.
An acoustic echo canceller (AEC) may be used to remove acoustic echo from an audio signal captured by a microphone, in order to facilitate improved communication. The AEC typically filters the microphone signal by determining an estimate of the acoustic echo, and subtracting the estimate from the microphone signal to produce an approximation of the true near-end signal. The estimate is obtained by applying a transformation to the far-end signal emitted from the loudspeaker. The transformation is implemented using an adaptive algorithm such as least mean squares, normalized least mean squares, or their variants, which are known to persons of ordinary skill in the art.
The adaptive transformation relies on a feedback loop, which continuously adjusts a set of coefficients that are used to calculate the estimated echo from the far-end signal. Different environments produce different acoustic echoes from the same loudspeaker signal, and any change in the local environment may change the way that echoes are produced. By using a feedback loop to continuously adjust the coefficients, an AEC can adapt its echo estimates to the local environment in which it operates.
While adaptation generally improves the echo estimates produced by an AEC, this is not always true. The feedback-based adaptation scheme works better in some situations than in others, so it may be beneficial to increase or decrease the rate of adaptation in different situations. The rate of adaptation may be controlled by adjusting a parameter referred to as “step size.” A larger step size will increase the rate of adaptation, and a smaller step size will decrease it.
When adaptation is first initiated, a relatively large step size is desirable because it will allow the AEC coefficients to quickly converge on a good approximation of the actual echo produced by the local environment. Once the AEC has converged, however, a smaller step size may be more desirable. With a smaller step size, any adjustments to the AEC coefficients will be less abrupt, and the feedback loop will therefore be less susceptible to disruptive inputs like background noise and double talk, which occurs when the far-end signal and the near-end signal are both simultaneously active.
Existing step size control schemes assume that the AEC will converge within a fixed period after system initialization. Accordingly, they employ a large step size during the fixed period and a smaller step size thereafter. However, convergence may occur after or substantially before the end of the fixed period, causing poor AEC performance in some circumstances.